vgg11_bn_cifar10.py

# Copyright (c) 2025 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#      http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

import torch
from torch import nn

__all__ = [
    "VGG",
    "vgg11_bn_cifar10",
    "vgg13_bn_cifar10",
    "vgg16_bn_cifar10",
    "vgg19_bn_cifar10",
]


class VGG(nn.Module):
    def __init__(self, features, num_classes=10, init_weights=True):
        super().__init__()
        self.features = features
        # CIFAR 10 (7, 7) to (1, 1)
        # self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.classifier = nn.Sequential(
            nn.Linear(512 * 1 * 1, 4096),
            # nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfgs = {
    "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "D": [
        64,
        64,
        "M",
        128,
        128,
        "M",
        256,
        256,
        256,
        "M",
        512,
        512,
        512,
        "M",
        512,
        512,
        512,
        "M",
    ],
    "E": [
        64,
        64,
        "M",
        128,
        128,
        "M",
        256,
        256,
        256,
        256,
        "M",
        512,
        512,
        512,
        512,
        "M",
        512,
        512,
        512,
        512,
        "M",
    ],
}


def _vgg(arch, cfg, batch_norm, pretrained, progress, device, **kwargs):
    if pretrained:
        kwargs["init_weights"] = False
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
    if pretrained:
        script_dir = os.path.dirname(__file__)
        state_dict = torch.load(script_dir + "/state_dicts/" + arch + ".pt", map_location=device, weights_only=False)
        model.load_state_dict(state_dict)
    return model


def vgg11_bn_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _vgg("vgg11_bn", "A", True, pretrained, progress, device, **kwargs)


def vgg13_bn_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    """VGG 13-layer model (configuration "B") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _vgg("vgg13_bn", "B", True, pretrained, progress, device, **kwargs)


def vgg16_bn_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    """VGG 16-layer model (configuration "D") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _vgg("vgg16_bn", "D", True, pretrained, progress, device, **kwargs)


def vgg19_bn_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    """VGG 19-layer model (configuration 'E') with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _vgg("vgg19_bn", "E", True, pretrained, progress, device, **kwargs)